Snapper (Lutjanus sp.) is an economically important fish for local fishermen in Banyuasin coastal water of South Sumatra. However, the current and historical stock of this species is still unknown. This study was aime...
Snapper (Lutjanus sp.) is an economically important fish for local fishermen in Banyuasin coastal water of South Sumatra. However, the current and historical stock of this species is still unknown. This study was aimed to estimate the stock status of Lutjanus sp. in the Banyuasin coastal waters. The annual catch and effort data were analyzed from 2008 to 2016. The different surplus production models were tested to obtain the best-fitted model based on the sign suitability test, model performance test, and multiple criteria analysis. The results indicated that the best-fitted model for Lutjanus sp. was the Fox model. The model had the best value for the determination coefficient (R2 = 97.2%), Nash-Sutcliffe Efficiency (-0.277), Mean Absolute Deviation (29.198), Mean Square Error (1,190.522), Root Mean Square Error (34.504), and RMSE-observations Standard Deviation Ratio (1.13), whereas the value of Mean Absolute Percentage Error (0.05) was the second-best value. The optimum effort (Eopt), maximum sustainable catch (CMSY), and total allowable catch were 22.236 trips/year, 623 ton and 498 ton/year, respectively. Based on plotting the effort and exploitation level (141%; 102%) in 2016, the stock status of Lutjanus sp. indicated depleting stock, the high fishing pressure and could encourage overfishing stock in the future.
Continuous glucose monitoring(CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and e...
Continuous glucose monitoring(CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and effective decision making for diabetes management. Here, we developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of CGM data to represent individual's intrinsic metabolic state and enable clinical applications. During pretraining, CGMformer encodes glucose dynamics including glucose level, fluctuation, hyperglycemia, and hypoglycemia into latent space with self-supervised learning. It shows generalizability in imputing glucose value across five external datasets with different populations and metabolic states(MAE = 3.7 mg/d L). We then fine-tuned CGMformer towards a diverse panel of downstream tasks in the screening of diabetes and its complications using task-specific data, which demonstrated a consistently boosted predictive accuracy over direct fine-tuning on a single task(AUROC = 0.914 for type 2 diabetes(T2D) screening and 0.741 for complication screening). By learning an intrinsic representation of an individual's glucose dynamics,CGMformer classifies non-diabetic individuals into six clusters with elevated T2D risks, and identifies a specific cluster with lean body-shape but high risk of glucose metabolism disorders, which is overlooked by traditional glucose measurements. Furthermore, CGMformer achieves high accuracy in predicting an individual's postprandial glucose response with dietary modelling(Pearson correlation coefficient = 0.763)and helps personalized dietary recommendations. Overall, CGMformer pretrains a transformer neural network architecture to learn an intrinsic representation by borrowing information from a large amount of daily glucose profiles, and demonstrates predictive capabilities fine-tuned towards a broad range of downstream applications, holding promise for the ear
The purpose of this study is to create an application which functions automatically with high accuracy when analyzing bank customer data. This needed due to non-perforMing loans occurring frequently caused by the inac...
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The purpose of this study is to create an application that functions automatically with high accuracy when analyzing bank customer data. This needed due to non-perforMing loans occurring frequently caused by the inacc...
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Decision-making processes in determining loan eligibility are often subjective which leads to imprecise credit predictions. Due to inaccurate inquiry on prospective customers done by field survey officers of Bank Perk...
Decision-making processes in determining loan eligibility are often subjective which leads to imprecise credit predictions. Due to inaccurate inquiry on prospective customers done by field survey officers of Bank Perkreditan Rakyat (BPR) Bandung City, it has experienced credit complications such as bad credits. Therefore, this study aims to help decision-makers in determining creditworthiness and preventing bad credits from occurring. To realize this solution, the study uses the Fuzzy Logic method to calculate the creditworthiness of each prospective loaner based on the inquiries done in the field survey. Fuzzy Logic is known to be a 'counting' methodology with varying words. In addition, it can implement human expertise into machine language with ease and adequately. Based on numerous testing performed, the results demonstrate a level of 90% in accuracy when inputting within the valid ranges of each fuzzy set and membership function. However, the level of accuracy is only based on the clarification result which is determined by a researcher and BPR director, not a general level of accuracy for other microfinance institutions. Nevertheless, the findings of this study prove the method has a high enough accuracy to support decision-makers in determining the loan eligibility of prospective loaners and through this application in the surveying process, survey workers can work more efficiently. Hence, in future has a higher chance of predicting bad credits from potential loaners.
Ihan or Ikan Batak (Neolissochillus thienemanni sumateranus), the heritage of Batak tribe, is considered as vulnerable species of freshwater fish in Indonesia, especially at Lake Toba. This study aims to introduce SMA...
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There are cases of patients with Acute Respiratory Infection (ARI) which are not handled quickly and appropriately at emergency department (ED), thus there should be a medium that can assist medical personnel in makin...
There are cases of patients with Acute Respiratory Infection (ARI) which are not handled quickly and appropriately at emergency department (ED), thus there should be a medium that can assist medical personnel in making decisions. This research proposes the development model of Clinical Group Decision Support systems (CGDSS) named KLISPA models. KLISPA model can accommodate a screening process to the working diagnosis process of ARI by integrating Case Based Reasoning (CBR) and Groups Decision Support system (GDSS). Screening process completed by CBR using Nearest Neighbor method. This process helps medical personnel such as nurses, co asst doctors, and general practitioners to do screening to patients for determining the early solution to the disease. Meanwhile, the working diagnosis solved by GDSS uses the methods of Eckenrode, Extended TOPSIS and BoostVote. This process accommodates the needs of joint decision-making which involves groups of Decision Maker (DM) consisting of pediatrician, child lung specialist doctor, and the radiologist doctor.
Precision prevention embraces personalized prevention but includes broader factors such as social determinants of health to improve cardiovascular health. The quality, quantity, precision, and diversity of data relata...
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The purpose of this study was to assess the fish biodiversity of Sungsang estuaries in South Sumatra. The species diversity, evenness, dominance, degree of similarity, and composition of fish communities as well as so...
The purpose of this study was to assess the fish biodiversity of Sungsang estuaries in South Sumatra. The species diversity, evenness, dominance, degree of similarity, and composition of fish communities as well as some physicochemical conditions were analyzed in order to establish the baseline data inventory of Sungsang estuaries. The results show that all of the physico-chemical parameters were in good condition for fish sustainability. Forty-eight (48) species of fish belonging to 29 families of freshwater, brackish water and marine sources were encountered in the water bodies. Johnius belangerii, Johnius amblycephalus and Setipinna taty were species with a relatively high abundance, but the appearance frequency of these species was high, medium and low, respectively. The value of the Shannon's diversity index for fish resources was classified as moderate (H'=1.477-2.708). The index value of evenness was classified as high (J'= 0.616 – 0.876), while the index value of dominance was classified as low (D = 0.097 – 0.382). This result indicate that the species diversity was good enough, the species spread was evenly distributed, that there was a stable community structure and no domination.
The research intends to create an application which is able to analyse sales data in a motorcycle company to predict the types of spare parts which should be stocked. This prediction is crucial since problems are ofte...
The research intends to create an application which is able to analyse sales data in a motorcycle company to predict the types of spare parts which should be stocked. This prediction is crucial since problems are often encountered while restocking. For instance, when there have been some imprecisions occurring in deciding regarding the types of spare parts to restock, the spare parts accumulate. It can cause inefficiency in terms of storage, the products quality deteriorates due to having been stored for too long, and sometimes the best-selling products are not available in the warehouse. This application is developed with Naïve Bayes Classifier (NBC) method which has a high accuracy in predicting future occurrences. This method works by calculating the probability value in each attribute class and determining the optimal probability value. From the test results, 4500 training data with 200 sample test data has 90% similarity with the results of the restock decision without application. For 500 test data, the similarity was 96%. It is proven that this method has a high accuracy so that it can help the decision makers solved the company problem in predicting the types of motorcycle parts to be restocked.
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